Real-time identified chaotic plants using neural enhanced learning machine technique. Issue 6 (8th January 2021)

• Record Type: Journal Article
• Authors: Anh, Ho Pham Huy
• Is Part Of: Engineering computations. Volume 38:Issue 6(2021)
• Page Start: 2810
• Page End: 2832
• Publication Date: 2021-01-08
• Subjects: Chaotic benchmark tests -- Neural-based enhanced extreme learning machine (EELM) algorithm -- Online adaptive rule implementation -- Single layer feed-forward neural (SLFN) model -- Uncertain nonlinear system identification -- Lyapunov stability principle; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-01-2020-0049
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: This paper aims to propose a new neural-based enhanced extreme learning machine (EELM) algorithm, used as an online adaptive estimation model, regarding undetermined system dynamics and containing internal/external perturbations. Design/methodology/approach: The EELM structure bases on the single layer feed-forward neural (SLFN) model in which the hidden weighting coefficients are initiated in random and the weighting outputs of the SLFN are online modified using an online adaptive rule implemented from Lyapunov stability concept. Findings: Four different benchmark uncertain chaotic system tests have been satisfactorily investigated for demonstrating the superiority of proposed EELM technique. Originality/value: Authors confirm that this manuscript is original.